The present invention relates to medical devices and more particularly to an improvement in EEG (electroencephalograph) devices used for evoked potential (EP) analysis. EP's are transient oscillations of the EEG which are time locked to the presentation of sensory stimulation. The waveshape of this oscillation, imbedded in the EEG, reflects the processing of that stimulus information by the brain. Successive components of the EP waveshapes represent the passage of incoming neuronal activity along particular anatomical pathways. For this reason, precise knowledge about EP waveshapes can provide a sensitive index of the functional status of neuroanatomical structures. In order to utilize such information most advantageously, for example, to monitor the condition of certain brain regions during neurosurgery, the EP waveshape must be separated and extracted from the other electrical activity in which it is embedded as quickly and as cleanly as possible. Because EP waveshapes are complex and vary depending upon stimulus parameters, neural condition and characteristic of individual patients, it is also advantageous to obtain objective criteria for evaluation of changes in EP waveshape during a critical period of observation, such as during an operation on the brain. At times, it is desirable to obtain such EP's from as many as 19 electrodes simultaneously, to compare the response in different kinds of stimuli sequentially in order to evaluate different neuroanatomical pathways. A major problem in such analyses is the poor signal-to-noise ratio.
One method that is used to improve the signal/noise ratio of evoked potentials is signal averaging. In evoked potential (EP)analysis, a large number of stimuli, such as light flashes or auditory clicks, are presented to the patient in a regular pattern, for example, 2048 auditory clicks at repetition rates about 7-10/second. The brain response, for example, the brain stem auditory evoked potential (BAEP), is in synch with the stimuli, but the noise is random. When the responses are averaged, the noise tends to cancel itself out, leaving an improved signal/noise ratio. This improvement is proportional to the square root of the sample size. Because the time required to achieve useful improvement of S/N is so long relative to the time frame of intraoperative events, conventional signal averaging is poorly suited for surgical monitoring. Further, significant fluctuations in the functional status of brain regions may occur during the long period required to accumulate a sufficiently large sample and the corresponding heterogeneous waveshapes are obscured by combination within the average EP finally obtained.
Even with signal averaging, the signal/noise ratio may not be sufficient for reproducible results, under some circumstances, as suggested in an article by Drs. E.R. John, H. Baird, J. Friedman and M. Bergelson entitled "Normative Values For Brain Stem Auditory Evoked Potentials Obtained By Digital Filtering And Automatic Peak Detection", Electroencephalography and Clinical Neurophysiology 1982, 54:153-160 (1982, Elsevier Sci. Pub. 0013-4949). Further improvement of the signal/noise ratio by increasing the sample size is prohibitively time-consuming.
An article entitled "Application of Digital Filtering and Automatic Peak Detection to Brain Stem Auditory Evoked Potential", Friedman, John, Bergelson, Kaiser, Baird; Electroencephalography and Clinical Neurophysiology, 1982, describes a way to achieve rapid improvement of S/N by using a digital bandpass filter with optimal bandwidth to suppress some noise components. This article describes an analysis of averaged brain stem auditory evoked responses. The same method can be applied to any type of EP. Repeated samples of signal (presence of stimulus) and noise (absence of stimulus) were subjected to FFT (Fast Fourier Transform). At each frequency, the variance of phase was computed separately for the sets of signal samples and noise samples. The optimal frequency band, for digital filtering, was obtained by comparing phase variance (as a function of frequency) in the absence of stimulus against phase variance in the presence of stimulus. Phase variance is low at frequencies which contribute to the waveshape of the evoked potential (which is phase-locked to the stimulus), while the phase variance of noise components is high because of its random composition. The optimal digital filter is defined as the frequency band within which the phase variance is lowest for samples of signal and highest for samples of noise. Once the filter ("filter window") has been selected, subsequent samples of signal are decomposed by FFT (Fast Fourier Transform) and an Inverse Fast Fourier Transform (IFFT) is then performed using only the terms inside the selected filter window. The signal which has been decomposed into its spectral components by FFT is thus reconstructed with the noise selectively removed. In contrast to conventional signal averaging methods, in which noise is reduced by summation of random variations, optimal digital filtering is selective removal of noise. It should be noted, however, that some noise components generated by the stimulator of the EP apparatus or reflecting high harmonics of other apparatus may have relatively low variance within the selected frequency domain.
In order to use this technique, the frequency "window" (band pass of frequencies) of the optimum filter was selected, using visual inspection by the operator, based on his visual reading of the phase variance diagrams and his experience. Those frequencies below and above the selected band were canceled (band rejection). The operator, if experience and careful, was able to select a frequency band that would improve the signal/noise ratio by selecting a band pass in which the signal was relatively strong compared to the noise.
That system has two major shortcomings. The first is that it requires the operator to visually inspect the phase diagram to select the optimal filter bandwidth (frequency window) so that the system relies upon the judmgent, skill and attention of a human operator. However, the time of such skilled operators is expensive, such a person may not be available during every surgical operation, and the person's judgement and attention may be less than perfect at times, especially during a prolonged operation.
The second problem is that, even if the band pass is correctly selected, it does not eliminate noise which is in phase synchronism with the signal. Such phase synchronous noise may lie within the optimal band pass. For example, components of stationary noise, such as from the harmonics of 60 Hz from operating room instruments or the evoked potential apparatus itself, may be in relatively stable phase and at the same frequency as components of the brain wave signal that it is desired to detect. Such synchronous noise is reincorporated into the signal, instead of being reduced by the filtering and averaging process.